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Integrating spatially explicit indices of abundance and habitat quality: an applied example for greater sage‐grouse management

Overview of attention for article published in Journal of Applied Ecology, November 2015
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  • Good Attention Score compared to outputs of the same age (72nd percentile)

Mentioned by

7 tweeters
2 Facebook pages


28 Dimensions

Readers on

104 Mendeley
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Integrating spatially explicit indices of abundance and habitat quality: an applied example for greater sage‐grouse management
Published in
Journal of Applied Ecology, November 2015
DOI 10.1111/1365-2664.12558
Pubmed ID

Peter S. Coates, Michael L. Casazza, Mark A. Ricca, Brianne E. Brussee, Erik J. Blomberg, K. Benjamin Gustafson, Cory T. Overton, Dawn M. Davis, Lara E. Niell, Shawn P. Espinosa, Scott C. Gardner, David J. Delehanty


Predictive species distributional models are a cornerstone of wildlife conservation planning. Constructing such models requires robust underpinning science that integrates formerly disparate data types to achieve effective species management.Greater sage-grouse Centrocercus urophasianus, hereafter 'sage-grouse' populations are declining throughout sagebrush-steppe ecosystems in North America, particularly within the Great Basin, which heightens the need for novel management tools that maximize the use of available information.Herein, we improve upon existing species distribution models by combining information about sage-grouse habitat quality, distribution and abundance from multiple data sources. To measure habitat, we created spatially explicit maps depicting habitat selection indices (HSI) informed by >35 500 independent telemetry locations from >1600 sage-grouse collected over 15 years across much of the Great Basin. These indices were derived from models that accounted for selection at different spatial scales and seasons. A region-wide HSI was calculated using the HSI surfaces modelled for 12 independent subregions and then demarcated into distinct habitat quality classes.We also employed a novel index to describe landscape patterns of sage-grouse abundance and space use (AUI). The AUI is a probabilistic composite of the following: (i) breeding density patterns based on the spatial configuration of breeding leks and associated trends in male attendance; and (ii) year-round patterns of space use indexed by the decreasing probability of use with increasing distance to leks. The continuous AUI surface was then reclassified into two classes representing high and low/no use and abundance. Synthesis and applications. Using the example of sage-grouse, we demonstrate how the joint application of indices of habitat selection, abundance and space use derived from multiple data sources yields a composite map that can guide effective allocation of management intensity across multiple spatial scales. As applied to sage-grouse, the composite map identifies spatially explicit management categories within sagebrush steppe that are most critical to sustaining sage-grouse populations as well as those areas where changes in land use would likely have minimal impact. Importantly, collaborative efforts among stakeholders guide which intersections of habitat selection indices and abundance and space use classes are used to define management categories. Because sage-grouse are an umbrella species, our joint-index modelling approach can help target effective conservation for other sagebrush obligate species and can be readily applied to species in other ecosystems with similar life histories, such as central-placed breeding.

Twitter Demographics

The data shown below were collected from the profiles of 7 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 104 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Mexico 1 <1%
United States 1 <1%
France 1 <1%
Germany 1 <1%
Unknown 100 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 28 27%
Student > Ph. D. Student 18 17%
Student > Master 18 17%
Student > Bachelor 6 6%
Other 4 4%
Other 18 17%
Unknown 12 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 56 54%
Environmental Science 25 24%
Unspecified 2 2%
Biochemistry, Genetics and Molecular Biology 2 2%
Veterinary Science and Veterinary Medicine 1 <1%
Other 2 2%
Unknown 16 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 December 2015.
All research outputs
of 15,996,627 outputs
Outputs from Journal of Applied Ecology
of 3,066 outputs
Outputs of similar age
of 287,512 outputs
Outputs of similar age from Journal of Applied Ecology
of 61 outputs
Altmetric has tracked 15,996,627 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 3,066 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 21.5. This one is in the 37th percentile – i.e., 37% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 287,512 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 61 others from the same source and published within six weeks on either side of this one. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.